The 3A Contextual Ranking System: Simultaneously Recommending Actors, Assets, and Group Activities

In this paper, we propose a personalized and contextual ranking algorithm implemented on top of the 3A interaction model. The latter is a generic model intended for designing and describing social and collaborative learning platforms integrating Actors, Assets and group Activities (the 3 “A”). The target user’s interactions with his/her environment are modeled in a heterogeneous graph. Then, the algorithm is applied to simultaneously rank actors, assets and group activities taking into account the target user’s context. As an illustrative application and a preliminary evaluation, we apply the algorithm on data related to the activities carried out in a European Research Project, especially the collaboration between its members through the joint production of deliverables in workpackages.


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Name: Proceedings of the 2009 ACM Conference on Recommender Systems, RecSys 2009
Presented at:
Recommender Systems 2009, New York, October 22-25, 2009
Year:
2009
Publisher:
ACM
Keywords:
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 Record created 2009-08-11, last modified 2018-03-17

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